A gated temporal-separable attention network for EEG-based depression recognition

Abstract

Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed in this paper for depression recognition. GTSAN model extracts discriminative information from EEG recordings in two ways. On the one hand, the gated recurrent unit (GRU) is used in the GTSAN model to capture the EEG historical information to form the features. On the other hand, the model digs the multilevel information by using an improved version of temporal convolutional network (TCN), called temporal-separable convolution network (TSCN), which applies causal convolution and dilated convolution to extract features from fine to coarse scales. The TSCN and GRU features can be produced in parallel. Finally, the new model introduces the attention mechanism to give different weights to these features, allowing them to be used to identify depression more effectively. Experiments on two depression datasets have demonstrated that the proposed model can mine potential depression patterns in data and obtain high recognition accuracies. The proposed model provides the possibility of using an EEG-based system to assist for diagnosing depression.